Feature extraction of underwater target in auditory sensation area based on MFCC

Wang, Wenbo; Li, Sichun; Yang, Jianshe; Zhao, Lei; Zhou, Weicun

doi:10.1109/coa.2016.7535736

Cited by 25 publications

(7 citation statements)

References 3 publications

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“…Anchors are fitted with high-power transmitters and can accurately acquire location data using GPS coordinates. In [98], the author's used a new technique which is based on Mel Frequency Cepstral Coefficients (MFCCs). The underwater acoustic method is generally nonlinear and very hard to evaluate, so a correct nonlinear algorithm is required.…”

Section: Range-free Algorithmmentioning

confidence: 99%

A Review of Underwater Localization Techniques, Algorithms, and Challenges

et al. 2020

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Recently, there has been increasing interest in the field of underwater wireless sensor networks (UWSNs), which is a basic source for the exploration of the ocean environment. A range of military and civilian applications is anticipated to assist UWSN. The UWSN is being developed by the extensive wireless sensor network (WSN) applications and wireless technologies. Therefore, in this paper, a review has been presented which unveils the existing challenges in the underwater environment. In this review, firstly, an introduction to UWSN is presented. After that, underwater localizations and the basics are presented. Secondly, the paper focuses on the architecture of UWSN and technologies used for underwater acoustic sensor network (UASN) localization. Various localization techniques are discussed in the paper classified by centralized and distributed localizations. They are further classified into estimated and prediction-based localizations. Also, various underwater localization algorithms are discussed, which are grouped by the algorithms based on range and range-free schemes. Finally, the paper focuses on the challenges existing in underwater localizations, underwater acoustic communications with conclusions.

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Section: Range-free Algorithmmentioning

confidence: 99%

A Review of Underwater Localization Techniques, Algorithms, and Challenges

et al. 2020

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“…Chinchu and Supriya [2] constructed a real time underwater target recognition system in which MFCCs were used for feature extraction, the SVM method was employed as the classification algorithm and the entire system was implemented using Labview. Wang et al [21] presented a feature extraction algorithm which focused on the MFCC feature coefficients of underwater targets and the radiated noise of different marine life (whales, sea lions, dolphins), divers, boats, and ships were studied, demonstrating that MFCCs can be effective in feature extraction and recognition. Zhang et al [22] showed that the features of MFCCs, firstorder differential MFCCs, and second-order differential MFCCs can be effectively used to recognise different underwater targets, and the recognition rate can be improved by combining features.…”

Section: Related Workmentioning

confidence: 99%

“…The classification of underwater acoustic signals requires the extraction of the feature parameters of the original signal so as to achieve fast, accurate, and stable decisions on signal classification. The feature parameters currently used are: (1) the time domain waveform feature parameter and (2) the spectral analysis feature parameter, including such widely used methods as the line spectral feature, LOFAR spectrum diagram, DEMON spectrogram, stealth features, high-order spectra, and so on [5,14]; (3) the time-frequency analysis feature parameter; (4) the nonlinear feature parameter, which is the reflection of attractor topological structures in the reconstructed phase space of the target noise signal; and (5) the auditory feature parameter extraction including the auditory cepstrum coefficient (ACC), the MFCC, the linear prediction cepstrum coefficient (LPCC), and so on [2,21,22]. Extraction of these feature parameters is a method for the classification and recognition of underwater acoustic signals according to the mechanism of human hearing based on bionics, which is one of the main research directions in the processing of underwater acoustic signals.…”

Section: Introductionmentioning

confidence: 99%

A New Low SNR Underwater Acoustic Signal Classification Method Based on Intrinsic Modal Features Maintaining Dimensionality Reduction

Yang

Wei²,

et al. 2020

Polish Maritime Research

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The classification of low signal-to-noise ratio (SNR) underwater acoustic signals in complex acoustic environments and increasingly small target radiation noise is a hot research topic.. This paper proposes a new method for signal processing—low SNR underwater acoustic signal classification method (LSUASC)—based on intrinsic modal features maintaining dimensionality reduction. Using the LSUASC method, the underwater acoustic signal was first transformed with the Hilbert-Huang Transform (HHT) and the intrinsic mode was extracted. the intrinsic mode was then transformed into a corresponding Mel-frequency cepstrum coefficient (MFCC) to form a multidimensional feature vector of the low SNR acoustic signal. Next, a semi-supervised fuzzy rough Laplacian Eigenmap (SSFRLE) method was proposed to perform manifold dimension reduction (local sparse and discrete features of underwater acoustic signals can be maintained in the dimension reduction process) and principal component analysis (PCA) was adopted in the process of dimension reduction to define the reduced dimension adaptively. Finally, Fuzzy C-Means (FCMs), which are able to classify data with weak features was adopted to cluster the signal features after dimensionality reduction. The experimental results presented here show that the LSUASC method is able to classify low SNR underwater acoustic signals with high accuracy.

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“…In fact, the LPCC method imposes an incorrect structure on turbulent noise. Similarly, there are problems with the MFCC-as the adjacent frame feature is extracted independently, which ignores internal correlations within signals [Wang, Li, Yang et al (2016)]. Although the relation can be compensated by overlapping adjacent frames, there is no reasonable overlapping parameter for the real-world applications.…”

Section: Related Work 21 Underwater Target Noise Processingmentioning

confidence: 99%

ia-PNCC: Noise Processing Method for Underwater Target Recognition Convolutional Neural Network

Wang¹,

He²,

Sun³

et al. 2019

Computers, Materials &Amp; Continua

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Underwater target recognition is a key technology for underwater acoustic countermeasure. How to classify and recognize underwater targets according to the noise information of underwater targets has been a hot topic in the field of underwater acoustic signals. In this paper, the deep learning model is applied to underwater target recognition. Improved anti-noise Power-Normalized Cepstral Coefficients (ia-PNCC) is proposed, based on PNCC applied to underwater noises. Multitaper and normalized Gammatone filter banks are applied to improve the anti-noise capacity. The method is combined with a convolutional neural network in order to recognize the underwater target. Experiment results show that the acoustic feature presented by ia-PNCC has lower noise and are wellsuited to underwater target recognition using a convolutional neural network. Compared with the combination of convolutional neural network with single acoustic feature, such as MFCC (Mel-scale Frequency Cepstral Coefficients) or LPCC (Linear Prediction Cepstral Coefficients), the combination of the ia-PNCC with a convolutional neural network offers better accuracy for underwater target recognition.Great importance has been attached to underwater target recognition technology by the academic and application sectors since earlier 1950s. Scholars in the USA have studied it since 1960s and Feigenbaum et al. in Stanford university developed the underwater prediction expert system and the improved SIAP by extracting features of narrow-band signal with signal recognition spectrum and related algorithms, in addition, related submarines in the USA and Britain have been equipped with the recognition system [Purton, Kourousis, Clothier et al. (2014)]. After that, scholars in Japan developed the SK-8 underwater target warning system based on FFT system, which compares the target signal with the existed spectrum to judge the target type [Xiao, Cai and Liao (2006)]. A.J. Bonner et al. in Canada developed the expert analysis system called INTERSENSOR based on the vessel radiated noise signal [Araghi, Khaloozade and Arvan (2009)]. Wu et al. [Wu, Jing, Chen et al. (1998); Wu, Li and Chen (1999)] have combined the energy spectrum of vessel noise with traditional statistical theory to recognize target with clustering. Yang Desen proposed the three-factor theory and judgement for line spectrum [Gu and Yang (2004); Li and Yang (2007)]. Han Shuping proposed a target spectrum of Spatio-temporal joint to differentiate target spectrum and self-noise spectrum effectively [Shu and Ping (2009)]. Yang Chunying applied the multiresolution decomposition algorithm and wavelet transform theory in the extraction of underwater target recognition power spectrum, in which the variable scale features of wavelet transform are used to obtain the better frequency resolution compared with the traditional short time Fourier transform method, improving the feature extraction accuracy greatly [Xi, Zou, Yang et al. (2011)]. With the rapid development of recognition technology, such technologies as n...

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Feature extraction of underwater target in auditory sensation area based on MFCC

Cited by 25 publications

References 3 publications

A Review of Underwater Localization Techniques, Algorithms, and Challenges

A Review of Underwater Localization Techniques, Algorithms, and Challenges

A New Low SNR Underwater Acoustic Signal Classification Method Based on Intrinsic Modal Features Maintaining Dimensionality Reduction

ia-PNCC: Noise Processing Method for Underwater Target Recognition Convolutional Neural Network

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